Join the Department of Biomedical Informatics for an exclusive fall semester programming bootcamp. Discover essential programming, data analytics, and machine learning skills crucial for biomedical informatics. Special topics such as Bioinformatics and NLP will be briefly covered.

Gain hands-on programming experience and discover diverse career opportunities in biomedical informatics.

Don't miss this chance to excel in healthcare data analytics and shape the future of the industry.

https://bmi.stonybrookmedicine.edu/Bootcamp/Bootcamp-Fall-2025

Location: NCS 120

Abstract: Computational pathology has revolutionized cancer diagnosis and research through the analysis of digitized whole slide images (WSIs). However, their giga-pixel size creates two intertwined bottlenecks: computational inefficiency, as prohibitive GPU memory makes standard end-to-end (E2E) training infeasible, and label inefficiency, as expert annotation is tedious and expensive. This dissertation confronts both challenges through novel architectures, training paradigms, and self-supervised learning methods for efficient WSI analysis.
To improve computational efficiency, this dissertation first introduces a locally supervised learning paradigm that enables E2E training on entire WSIs by partitioning a network into gradient-isolated modules, circumventing the memory bottleneck of backpropagation. Second, it presents Prompt-MIL, a parameter-efficient fine-tuning framework training only a few prompts to guide large pre-trained models, reducing trainable parameters, memory, and training time. Third, this work proposes 2DMamba, the first intrinsic Mamba architecture that preserves the crucial 2D spatial structure of images, overcoming the spatial discrepancy in 1D models. Fourth, it presents Locally Bi-directional Mamba (LBMamba), whose hardware-aware local backward scan integrates bi-directional scanning into a single forward pass, improving the throughput-performance trade-off of Mamba models.
To improve label efficiency, this dissertation proposes a precise location based matching strategy for self-supervised dense contrastive learning, which allows a local patch in one augmented view to match multiple overlapping patches in another, producing more accurate correspondences and superior features for dense prediction tasks like segmentation and detection. Additionally, to better scale multi-channel cell imaging modalities, this dissertation introduces ChannelSFormer, a channel-agnostic vision transformer that disentangles spatial and channel-wise reasoning through divided attention and channel class token, enabling effective representation learning across variable channel configurations in both self-supervised and supervised settings.
In summary, this dissertation presents a holistic investigation into the efficiency bottlenecks in computational pathology. Through these combined contributions in model architecture, training paradigms, and self-supervised learning, this work establishes a more scalable, efficient, and powerful computational framework for analyzing giga-pixel pathology images.

Speaker: Jingwei Zhang

Location: NCS 220

Zoom: https://stonybrook.zoom.us/j/93175806292?pwd=xbtxnQyYGoThz5B1DyJxJxPF9lxiJE.1
Meeting ID: 931 7580 6292
Passcode: 314091
Department of Ecology and Evolution Colloquium Arthur Porto is Assistant Curator of Al for Biodiversity at the Florida Museum of Natural History. His research sits at the intersection of evolutionary biology, computer vision, and machine learning, with a focus on developing open, scalable methods for extracting biological information from images and natural- history collection objects. Life Sciences Building, Room 038
Zoom Link: https://github.com/giorgianb/spdhackspring2021/blob/main/bit.ly/spdhack2021

ΣΦΔ Hack Spring 2021 is ΣΦΔ's first annual machine learning hackathon. ΣΦΔ Hack Spring 2021 aims to introduce Stony Brook students to the rich and challenging field of machine learning, and develop the skills necessary to build sophisticated machine learning models on their own.
 
More info here: https://github.com/giorgianb/spdhackspring2021/blob/main/README.md
Qualitative data can be challenging to analyze and interpret effectively. In this workshop, SBU Libraries' Data Literacies Lead, Ahmad Pratama will show you how to extract meaningful insights from textual data, including understanding sentiment trends. Learn to explore qualitative data with Python using word clouds, basic natural language processing (NLP) techniques, and lexicon-based sentiment analysis with VADER.
RSVP via link: https://t.e2ma.net/click/t70ivh/5wwlu4oe/hy5q96

AI3 Seminar

Meir Feder

Professor, School of Electrical Engineering
Jokel Chair in Information Theory, School of Electrical and Computer Engineering
Tel-Aviv University

Information-Theoretic Framework for Understanding Modern Machine-Learning

Abstract:

Information Theory views learning as universal prediction under log loss, characterized through regret bounds. Unlike the classical results that considered ``small'' model classes and provided uniform regret, the proposed framework provides non-uniform, model dependent bounds utilizing an effective notion of architecture-based model complexity. This complexity is defined by the probability mass or volume of the set of all models in the vicinity of the target model \theta_0, in an informational distance. This volume might be hard to evaluate, yet by local analysis it is related to spectral properties of the expected Hessian or the Fisher Information Matrix at \theta_0, leading to tractable approximations. We argue that successful architectures possess a broad complexity range, enabling learning in highly over-parameterized model classes. The framework sheds light on the role of inductive biases, the effectiveness of stochastic gradient descent (SGD) algorithm, and phenomena such as flat minima. It unifies online, batch, supervised, and generative settings, and applies across the stochastic-realizable and agnostic regimes. Moreover, it provides insights into the success of modern machine-learning architectures, such as deep neural networks and transformers, suggesting that their broad complexity range naturally arises from their layered structure. These insights open the door to the design of alternative architectures with potentially comparable or even superior performance.

Biography:

Meir Feder received the Sc.D. degree in Electrical Engineering and Ocean Engineering in 1987 from the Massachusetts Institute of Technology (MIT) and the Woods Hole Oceanographic Institution (WHOI). After being a Research Associate and a Lecturer at MIT, he joined the School of Electrical Engineering, Tel-Aviv University in 1990, where he is the Jokel Chaired Professor and the former founding head of Tel-Aviv university center for Artificial intelligence and Data science (TAD). Parallel to his academic career, he is closely involved with the high-tech industry: he founded 5 companies, among them Peach Networks (Acq: MSFT) and Amimon (Acq:LON.VTC). Recently, with his renewed interest in machine learning and AI, he co-founded Run:ai (Acq:NVDA), a virtualization, orchestration, and acceleration platform for AI infrastructure, acquired by Nvidia to support its GPU cloud operation.

Prof. Feder received several academic and professional awards including the IEEE Information Theory Society best paper award, the Padovani lectureship, the creative thinking award of the Israeli Defense Forces, and the Research Prize of the Israeli Electronic Industry, awarded by the President of Israel. For the technology he developed in Amimon, he received the 2020 Scientific and Engineering Award of the Academy of Motion Picture Arts and Sciences (OSCAR) and was announced the principal inventor of the technology that attained the 73rd Engineering Emmy Award of the Television Academy.

Location: NCS120

Abstract: In this talk, we take a step back and argue that many varied and seemingly unrelated attacks on the web are actually symptoms of one deeper problem that has existed since the web's inception. Whether it is attacks due to expired domain names, cloaking done by malicious websites, malvertising, or even our growing distrust of the news can be largely attributed back to the issue of stateless linking. Stateless linking refers to the absence of any integrity guarantees between the time that a link for a remote resource was created, to a future time when this link is resolved by web clients. We draw on 10+ years of research to demonstrate how stateless linking and the resulting lack of content integrity is the true culprit for many of our past, current, and likely future web problems. Successfully tackling this one really challenging problem, has the potential of solving many of our web woes.

Bio: Nick Nikiforakis is affiliated with the National Security Institute. He received his PhD in Computer Science from KU Leuven in Belgium. He received his MSc, in Parallel and Distributed Systems and BSc in Computer Science from the University of Crete, Greece. His research focuses on web security and privacy, software security, and intrusion detection.

Location: NCS 120
Join the Conversation: Share Your Thoughts about Learning, Academics, and AI

The world of college is changing fast, and Artificial Intelligence (AI) is at the center of it. We are part of the Institute on AI, Pedagogy, and the Curriculum with AAC&U, and we need to hear from the people AI affects most: you!

This is an open discussion for all students to share their honest experiences, their top concerns, and their best ideas about AI in our academic environment. We'll be diving into these key questions:
  • How can AI actually make learning better or easier? What opportunities do you see for using AI tools to enhance your assignments, research, or skills?
  • What are your biggest worries about AI? Is it about cheating, being graded fairly, or preparing for the job market? How is AI impacting your workload or stress levels?
  • What specific tools, workshops, or policies would help you use AI responsibly and successfully? (Think training, software, or clear rules.)
Date: Monday, December 1st
Time: 12:30pm-1:45pm
Location: West Campus - Location TBD
or
Date: Wednesday, December 3rd
Time: 10:30am-11:45am
Location: East Campus - HSC 2-154B

Please register in advance so we can confirm the room.

Note: Videos will not be shared publicly and comments will only be shared in aggregate.

Your voice matters. Come tell us how AI is affecting your studies, your stress, and your success!
  • Dr. Rose Tirotta-Esposito (Assistant Provost; Director of CELT)
  • Dr. Elizabeth Hewitt (Associate Professor in the Department of Technology and Society (DTS) in the College of Engineering and Applied Sciences)
  • Chris Kretz (Associate Librarian and Head of Academic Engagement at SBU Libraries)
  • Prof. Rajiv Lajmi (Assistant Professor in the School of Health Professions and Chair of Applied Health Informatics)
  • Dr. Matthew Salzano (Assistant Professor in the Department of Communication in the School of Communication and Journalism)